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The Crystal Ball - Instruction Manual I: Introduction to Data Science

Nguồn ngoài

The Crystal Ball - Instruction Manual I: Introduction to Data Science

A perfect introduction to the exploding field of Data Science for the curious, first-time student. The author brings his trademark conversational tone to the important pillars of the discipline: exploratory data analysis, choices for structuring data, causality, machine learning principles, and introductory Python programming using open-source Jupyter Notebooks.

Data ScienceEngineering
Tác giả
Stephen Davies
Nhà xuất bản
University of Mary Washington
Nguồn
LibreTexts
Giấy phép
CC BY-SA
Ngôn ngữ
EN
Danh mục
Công nghệ thông tin
Đọc trực tuyến Tải PDF (nguồn mở)
Mục lục· 131 mục
  1. 01: Introduction
  2. 1.01: Defining Data Science
  3. 1.02: A Word Of Warning
  4. 02: A trip to Jupyter
  5. 2.01: Jupyter Notebooks
  6. 2.02: Code and Output
  7. 03: Three Kinds of Atomic Data
  8. 3.01: Atomic Data
  9. 3.02: Environments and Variables
  10. 3.03: Atomic Data Types
  11. 3.04: The Three Kinds in Python
  12. 04: Memory pictures
  13. 4.01: A Picture of Memory
  14. 05: Calculations
  15. 5.01: Mathematical Operations
  16. 5.02: String Operations
  17. 5.03: Return Values
  18. 06: Scales of Measure
  19. 6.01: The Four Scales of Measure
  20. 6.02: Final Word
  21. 07: Three Kinds of Aggregate Data
  22. 7.01: Aggregate Data Types
  23. 7.02: Aggregate Data and Memory Pictures
  24. 08: Arrays in Python (1 of 2)
  25. 8.01: Packages
  26. 8.02: The NumPy 'ndarray'
  27. 8.03: Arrays in Memory Pictures
  28. 09: Arrays in Python (2 of 2)
  29. 9.01: Getting the Array Size
  30. 9.02: Accessing Individual Elements
  31. 9.03: Vectorized Arithmetic Operators
  32. 9.04: Copying and Not Copying Arrays
  33. 9.05: Sorting Arrays
  34. 9.06: More Exotic Array Modifications
  35. 9.07: Postlude- Characters Within a String
  36. 9.08: Summary
  37. 10: Interpreting Data
  38. 10.01: Independent and Dependent Variables
  39. 10.02: Association and Causality
  40. 10.03: Confounding Factors
  41. 10.04: Dealing with Confounding Factors
  42. 10.05: Spurious Associations
  43. 11: Associative Arrays in Python (1 of 3)
  44. 11.01: The Pandas Series
  45. 12: Associative Arrays in Python (2 of 3)
  46. 12.01: Accessing Individual Elements
  47. 12.02: Vectorized Arithmetic Operators
  48. 12.03: Copying Serieses
  49. 12.04: Sorting Serieses
  50. 12.05: Concatenating and Combining
  51. 12.06: Summary
  52. 13: Associative Arrays in Python (3 of 3)
  53. 13.01: Queries
  54. 14: Loops
  55. 14.01: The Two Species of Loops
  56. 14.02: A Word of Caution
  57. 14.03: Iterating Through an Array
  58. 14.04: Iterating Through the Values of a Series
  59. 14.05: Iterating Through the Keys of a Series
  60. 14.06: Iterating through the Keys and Values of a Series
  61. 14.07: Wrapping up
  62. 15: Exploratory Data Analysis- univariate
  63. 15.01: Categorical Data- Counts of Occurrences
  64. 15.02: Numerical data- Quantiles
  65. 15.03: Numerical Data- Other Summary Statistics
  66. 15.04: Plotting Univariate Data
  67. 15.05: Numerical Data- Histograms
  68. 15.06: Numerical Data- Box Plots
  69. 16: Tables in Python (1 of 3)
  70. 16.01: Reading a DataFrame from a .csv file
  71. 16.02: Missing Values
  72. 16.03: Removing Rows Columns
  73. 17: Tables in Python (2 of 3)
  74. 17.01: Section 1-
  75. 17.02: Section 2-
  76. 17.03: Section 3-
  77. 17.04: Section 4-
  78. 17.05: Section 5-
  79. 18: Tables in Python (3 of 3)
  80. 18.01: Section 1-
  81. 18.02: Section 2-
  82. 18.03: Section 3-
  83. 19: Exploratory Data Analysis: bivariate (1 of 2)
  84. 19.01: Section 1-
  85. 19.02: Section 2-
  86. 20: Exploratory Data Analysis: Bivariate (2 of 2)
  87. 20.01: Section 1-
  88. 20.02: Section 2-
  89. 20.03: Section 3-
  90. 20.04: Section 4-
  91. 20.05: Section 5-
  92. 20.06: Section 6-
  93. 21: Branching
  94. 21.1: The if statement
  95. 21.2: The if else Statement
  96. 21.3: The if elif else statement
  97. 21.4: Nesting
  98. 21.5 Combining branching with loops
  99. 22: Functions (1 of 2)
  100. 22.1: Why do all this
  101. 22.2: The 'def' statement
  102. 22.3: Writing vs. calling
  103. 22.4: Naming arguments
  104. 22.5: Passing Aggregate Data to Functions
  105. 22.6: Returning Text
  106. 22.7: Returning True or False
  107. 22.8: Multiple Return Statements
  108. 22.9: Returning Nothing At All
  109. 22.10: Calling a Function from Another Function
  110. 23: Functions (2 of 2)
  111. 24: Recoding and Transforming
  112. 24.1: Recoding with Simple Operations
  113. 24.2: Transforming with Simple Operations
  114. 24.3: More Complex Transformations
  115. 25: Machine Learning - Concepts
  116. 25.1: Data mining vs. Machine learning
  117. 25.2: Deductive vs. Inductive reasoning
  118. 25.3: Supervised vs. Unsupervised Learning
  119. 26: Classification - Concepts
  120. 26.1 Labeled and Unlabeled examples
  121. 26.2: Three Kinds of Examples
  122. 26.3: The Prior
  123. 27: Decision Trees (1 of 2)
  124. 27.1: A Working Example
  125. 27.2: Decision Trees
  126. 28: Decision Trees (2 of 2)
  127. 28.1: Decision Trees in Python
  128. 28.2: Decision Tree Induction
  129. 29: Evaluating a Classifier
  130. 29.1: What Doing Well Means
  131. 29.2: Calculating Accuracy in Python